# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import paddle
from paddle import nn, ParamAttr
from paddle.nn import functional as F
import numpy as np
import functools
from .tps import GridGenerator
'''This code is refer from:
https://github.com/hikopensource/DAVAR-Lab-OCR/davarocr/davar_rcg/models/transformations/gaspin_transformation.py
'''
class SP_TransformerNetwork(nn.Layer):
"""
Sturture-Preserving Transformation (SPT) as Equa. (2) in Ref. [1]
Ref: [1] SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition. AAAI-2021.
"""
def __init__(self, nc=1, default_type=5):
""" Based on SPIN
Args:
nc (int): number of input channels (usually in 1 or 3)
default_type (int): the complexity of transformation intensities (by default set to 6 as the paper)
"""
super(SP_TransformerNetwork, self).__init__()
self.power_list = self.cal_K(default_type)
self.sigmoid = nn.Sigmoid()
self.bn = nn.InstanceNorm2D(nc)
def cal_K(self, k=5):
"""
Args:
k (int): the complexity of transformation intensities (by default set to 6 as the paper)
Returns:
List: the normalized intensity of each pixel in [0,1], denoted as \beta [1x(2K+1)]
"""
from math import log
x = []
if k != 0:
for i in range(1, k+1):
lower = round(log(1-(0.5/(k+1))*i)/log((0.5/(k+1))*i), 2)
upper = round(1/lower, 2)
x.append(lower)
x.append(upper)
x.append(1.00)
return x
def forward(self, batch_I, weights, offsets, lambda_color=None):
"""
Args:
batch_I (Tensor): batch of input images [batch_size x nc x I_height x I_width]
weights:
offsets: the predicted offset by AIN, a scalar
lambda_color: the learnable update gate \alpha in Equa. (5) as
g(x) = (1 - \alpha) \odot x + \alpha \odot x_{offsets}
Returns:
Tensor: transformed images by SPN as Equa. (4) in Ref. [1]
[batch_size x I_channel_num x I_r_height x I_r_width]
"""
batch_I = (batch_I + 1) * 0.5
if offsets is not None:
batch_I = batch_I*(1-lambda_color) + offsets*lambda_color
batch_weight_params = paddle.unsqueeze(paddle.unsqueeze(weights, -1), -1)
batch_I_power = paddle.stack([batch_I.pow(p) for p in self.power_list], axis=1)
batch_weight_sum = paddle.sum(batch_I_power * batch_weight_params, axis=1)
batch_weight_sum = self.bn(batch_weight_sum)
batch_weight_sum = self.sigmoid(batch_weight_sum)
batch_weight_sum = batch_weight_sum * 2 - 1
return batch_weight_sum
class GA_SPIN_Transformer(nn.Layer):
"""
Geometric-Absorbed SPIN Transformation (GA-SPIN) proposed in Ref. [1]
Ref: [1] SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition. AAAI-2021.
"""
def __init__(self, in_channels=1,
I_r_size=(32, 100),
offsets=False,
norm_type='BN',
default_type=6,
loc_lr=1,
stn=True):
"""
Args:
in_channels (int): channel of input features,
set it to 1 if the grayscale images and 3 if RGB input
I_r_size (tuple): size of rectified images (used in STN transformations)
offsets (bool): set it to False if use SPN w.o. AIN,
and set it to True if use SPIN (both with SPN and AIN)
norm_type (str): the normalization type of the module,
set it to 'BN' by default, 'IN' optionally
default_type (int): the K chromatic space,
set it to 3/5/6 depend on the complexity of transformation intensities
loc_lr (float): learning rate of location network
stn (bool): whther to use stn.
"""
super(GA_SPIN_Transformer, self).__init__()
self.nc = in_channels
self.spt = True
self.offsets = offsets
self.stn = stn # set to True in GA-SPIN, while set it to False in SPIN
self.I_r_size = I_r_size
self.out_channels = in_channels
if norm_type == 'BN':
norm_layer = functools.partial(nn.BatchNorm2D, use_global_stats=True)
elif norm_type == 'IN':
norm_layer = functools.partial(nn.InstanceNorm2D, weight_attr=False,
use_global_stats=False)
else:
raise NotImplementedError('normalization layer [%s] is not found' % norm_type)
if self.spt:
self.sp_net = SP_TransformerNetwork(in_channels,
default_type)
self.spt_convnet = nn.Sequential(
# 32*100
nn.Conv2D(in_channels, 32, 3, 1, 1, bias_attr=False),
norm_layer(32), nn.ReLU(),
nn.MaxPool2D(kernel_size=2, stride=2),
# 16*50
nn.Conv2D(32, 64, 3, 1, 1, bias_attr=False),
norm_layer(64), nn.ReLU(),
nn.MaxPool2D(kernel_size=2, stride=2),
# 8*25
nn.Conv2D(64, 128, 3, 1, 1, bias_attr=False),
norm_layer(128), nn.ReLU(),
nn.MaxPool2D(kernel_size=2, stride=2),
# 4*12
)
self.stucture_fc1 = nn.Sequential(
nn.Conv2D(128, 256, 3, 1, 1, bias_attr=False),
norm_layer(256), nn.ReLU(),
nn.MaxPool2D(kernel_size=2, stride=2),
nn.Conv2D(256, 256, 3, 1, 1, bias_attr=False),
norm_layer(256), nn.ReLU(), # 2*6
nn.MaxPool2D(kernel_size=2, stride=2),
nn.Conv2D(256, 512, 3, 1, 1, bias_attr=False),
norm_layer(512), nn.ReLU(), # 1*3
nn.AdaptiveAvgPool2D(1),
nn.Flatten(1, -1), # batch_size x 512
nn.Linear(512, 256, weight_attr=nn.initializer.Normal(0.001)),
nn.BatchNorm1D(256), nn.ReLU()
)
self.out_weight = 2*default_type+1
self.spt_length = 2*default_type+1
if offsets:
self.out_weight += 1
if self.stn:
self.F = 20
self.out_weight += self.F * 2
self.GridGenerator = GridGenerator(self.F*2, self.F)
# self.out_weight*=nc
# Init structure_fc2 in LocalizationNetwork
initial_bias = self.init_spin(default_type*2)
initial_bias = initial_bias.reshape(-1)
param_attr = ParamAttr(
learning_rate=loc_lr,
initializer=nn.initializer.Assign(np.zeros([256, self.out_weight])))
bias_attr = ParamAttr(
learning_rate=loc_lr,
initializer=nn.initializer.Assign(initial_bias))
self.stucture_fc2 = nn.Linear(256, self.out_weight,
weight_attr=param_attr,
bias_attr=bias_attr)
self.sigmoid = nn.Sigmoid()
if offsets:
self.offset_fc1 = nn.Sequential(nn.Conv2D(128, 16,
3, 1, 1,
bias_attr=False),
norm_layer(16),
nn.ReLU(),)
self.offset_fc2 = nn.Conv2D(16, in_channels,
3, 1, 1)
self.pool = nn.MaxPool2D(2, 2)
def init_spin(self, nz):
"""
Args:
nz (int): number of paired \betas exponents, which means the value of K x 2
"""
init_id = [0.00]*nz+[5.00]
if self.offsets:
init_id += [-5.00]
# init_id *=3
init = np.array(init_id)
if self.stn:
F = self.F
ctrl_pts_x = np.linspace(-1.0, 1.0, int(F / 2))
ctrl_pts_y_top = np.linspace(0.0, -1.0, num=int(F / 2))
ctrl_pts_y_bottom = np.linspace(1.0, 0.0, num=int(F / 2))
ctrl_pts_top = np.stack([ctrl_pts_x, ctrl_pts_y_top], axis=1)
ctrl_pts_bottom = np.stack([ctrl_pts_x, ctrl_pts_y_bottom], axis=1)
initial_bias = np.concatenate([ctrl_pts_top, ctrl_pts_bottom], axis=0)
initial_bias = initial_bias.reshape(-1)
init = np.concatenate([init, initial_bias], axis=0)
return init
def forward(self, x, return_weight=False):
"""
Args:
x (Tensor): input image batch
return_weight (bool): set to False by default,
if set to True return the predicted offsets of AIN, denoted as x_{offsets}
Returns:
Tensor: rectified image [batch_size x I_channel_num x I_height x I_width], the same as the input size
"""
if self.spt:
feat = self.spt_convnet(x)
fc1 = self.stucture_fc1(feat)
sp_weight_fusion = self.stucture_fc2(fc1)
sp_weight_fusion = sp_weight_fusion.reshape([x.shape[0], self.out_weight, 1])
if self.offsets: # SPIN w. AIN
lambda_color = sp_weight_fusion[:, self.spt_length, 0]
lambda_color = self.sigmoid(lambda_color).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
sp_weight = sp_weight_fusion[:, :self.spt_length, :]
offsets = self.pool(self.offset_fc2(self.offset_fc1(feat)))
assert offsets.shape[2] == 2 # 2
assert offsets.shape[3] == 6 # 16
offsets = self.sigmoid(offsets) # v12
if return_weight:
return offsets
offsets = nn.functional.upsample(offsets, size=(x.shape[2], x.shape[3]), mode='bilinear')
if self.stn:
batch_C_prime = sp_weight_fusion[:, (self.spt_length + 1):, :].reshape([x.shape[0], self.F, 2])
build_P_prime = self.GridGenerator(batch_C_prime, self.I_r_size)
build_P_prime_reshape = build_P_prime.reshape([build_P_prime.shape[0],
self.I_r_size[0],
self.I_r_size[1],
2])
else: # SPIN w.o. AIN
sp_weight = sp_weight_fusion[:, :self.spt_length, :]
lambda_color, offsets = None, None
if self.stn:
batch_C_prime = sp_weight_fusion[:, self.spt_length:, :].reshape([x.shape[0], self.F, 2])
build_P_prime = self.GridGenerator(batch_C_prime, self.I_r_size)
build_P_prime_reshape = build_P_prime.reshape([build_P_prime.shape[0],
self.I_r_size[0],
self.I_r_size[1],
2])
x = self.sp_net(x, sp_weight, offsets, lambda_color)
if self.stn:
x = F.grid_sample(x=x, grid=build_P_prime_reshape, padding_mode='border')
return x